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This talk is about the description of the implementation of a Semantic Search
Engine based on Solr.
Meaningfully structuring content is critical, Natural Language Processing and
Semantic Enrichment is becoming increasingly important to improve the quality
of Solr search results .
Our solution is based on three advanced features :
Entity-oriented search - Searching not by keyword, but by entities (concepts
in a certain domain).
Knowledge graphs - Leveraging relationships amongst entities: Linked Data
datasets (Freebase, DbPedia, Custom ...)
Search assistance - Autocomplete and Spellchecking are now common features,
but using semantic data makes it possible to offer smarter features, driving
the users to build queries in a natural way.
The approach includes unstructured data processing mechanisms integrated with
Solr to automatically index semantic and multi-language information.
Smart Autocomplete will complete users' query with entity names and
properties from the domain knowledge graph. As the user types, the system
will propose a set of named entities and/or a set of entity types across
different languages. As the user accepts a suggestion, the system will
dynamically adapt following suggestions and return relevant documents.
Semantic More Like This will find similar documents to a seed one, based on
the underlying knowledge in the documents, instead of tokens.